Posted by
Hector Maletta on
Jul 07, 2012; 2:53am
URL: http://spssx-discussion.165.s1.nabble.com/How-to-use-weighed-data-for-a-generalized-linear-model-GzLM-analysis-tp5714060p5714064.html
There are several different questions or problems involved here.
1. Are weights appropriate for estimating generalized linear models? Some
think they are not. I'm undecided on that. With a simple random sample I'd
say OK, do not apply any weights; with the usual case of disproportionate
(random) sampling using stratification and (worse still) clustering, I am
not sure.
2. If you use inflationary or frequency weights, SPSS would think your
sample size is the weighted sample size, which is larger than your actual
sample size, thus underestimating the standard error of your estimates.
3. If you use just proportional weighting, such that the weighted sample
size equals the unweighted sample size, which is an approximate solution
(solving for disproportionate sampling but not for clustering) you'd still
have a problem with SPSS generalized linear models (apart from the problem
for computing standard errors if your sample involves clustering). The
problem you'll have with SPSS is that Generalized linear models in SPSS have
the nasty habit of rounding the weights BEFORE using them (unlike other
procedures that apply rounding to the final result, i.e. the weighted
frequencies, not to each particular case weight). Proportional weights mean
that some weights are greater than 1 and others are lower than 1, with an
overall mean weight of 1 (because the weights do not alter sample size).
Thus any weight below 0.5 will be rounded to zero (causing you to "lose"
cases), weights between 0.5 and 1.5 will all be rounded to 1, those from 1.5
to 2.5 will be rounded to 2, and so on, thus defeating a large part of the
purpose of weighting.
4. You may think of an apparently clever solution: not using weights at all,
and applying instead the SPSS Complex Samples facility on an unweighted
dataset in order to compute standard errors. But in that case you'll have
another problem: up to the current version (v.20) SPSS Complex Samples does
not cover Generalized Linear Models or Mixed Models.
So you're in a fix, I guess. Sorry for not being more helpful.
Hector
-----Mensaje original-----
De: SPSSX(r) Discussion [mailto:
[hidden email]] En nombre de Poes,
Matthew Joseph
Enviado el: Friday, July 06, 2012 17:45
Para:
[hidden email]
Asunto: Re: How to use weighed data for a generalized linear model (GzLM)
analysis?
I have not personally done this, but from what I have recently been told by
one of the IBM techs that frequents this forum, if you use the weight
variables for the scale weight, and the stratification variables for the
offset variable, this should effectively allow the use of weighted data.
Matthew J Poes
Research Data Specialist
Center for Prevention Research and Development University of Illinois 510
Devonshire Dr.
Champaign, IL 61820
Phone: 217-265-4576
email:
[hidden email]
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Sylvia
Sent: Friday, July 06, 2012 3:42 PM
To:
[hidden email]
Subject: How to use weighed data for a generalized linear model (GzLM)
analysis?
I am working with a data set that uses geographically stratified sample
design and therefore needs to use weighted data to generate accurate
standard errors.
I was wondering if any of you have used weighed data for a generalized
linear model in SPSS and could help me with the know-hows.
Thanks a ton!
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